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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 10 of 10 1 AnnoXplorer: A Scalable, Integrated Approach for the Visual Analysis of Text Annotations Baumann, Martin; Minasyan, Harutyun; Koch, Steffen... In: Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) (2020) BASE Show details 2 Annotating speaker stance in discourse:the Brexit Blog Corpus Simaki, Vasiliki; Paradis, Carita; Skeppstedt, Maria. - 2020 BASE Show details 3 Visual Analysis of Sentiment and Stance in Social Media Texts ... Kucher, Kostiantyn; Paradis, Carita; Kerren, Andreas. - : The Eurographics Association, 2018 BASE Show details 4 Evaluating stance-annotated sentences from political blogs regarding the Brexit:a quantitative analysis Simaki, Vasiliki; Kerren, Andreas; Paradis, Carita. - 2018 BASE Show details 5 Annotating speaker stance in discourse: the Brexit Blog Corpus (BBC) ... : Annotating speaker stance in discourse: the Brexit Blog Corpus (BBC) ... Kerren, Andreas; Paradis, Carita. - : Linnaeus University, 2017 BASE Show details 6 Brexit Blog Corpus (BBC) ... : Brexit Blog Corpus (BBC) ... Kerren, Andreas; Paradis, Carita. - : Linnaeus University, 2017 BASE Show details 7 Stance Classification in Texts from Blogs on the 2016 British Referendum Kerren, Andreas; Simaki, Vasiliki; Paradis, Carita. - : Springer, 2017 Abstract: The problem of identifying and correctly attributing speaker stance in human communication is addressed in this paper. The data set consists of political blogs dealing with the 2016 British referendum. A cognitive-functional framework is adopted with data annotated for six notional stance categories: contrariety, hypotheticality, necessity, prediction, source of knowledge, and uncertainty. We show that these categories can be implemented in a text classification task and automatically detected. To this end, we propose a large set of lexical and syntactic linguistic features. These features were tested and classification experiments were implemented using different algorithms. We achieved accuracy of up to 30% for the six-class experiments, which is not fully satisfactory. As a second step, we calculated the pair-wise combinations of the stance categories. The contrariety and necessity binary classification achieved the best results with up to 71% accuracy. URL: https://doi.org/10.1007/978-3-319-66429-3_70 https://eprints.lancs.ac.uk/id/eprint/124810/ BASE Hide details 8 Identifying the Authors’ National Variety of English in Social Media Text Simaki, Vasiliki; Simakis, Panagiotis; Paradis, Carita. - : Association for Computational Linguistics, 2017 BASE Show details 9 Visual Analysis of Text Annotations for Stance Classification with ALVA ... Kucher, Kostiantyn; Kerren, Andreas; Paradis, Carita. - : The Eurographics Association, 2016 BASE Show details 10 Visual analysis of online social media to open up the investigation of stance phenomena Kucher, Kostiantyn; Schamp-Bjerede, Teri; Kerren, Andreas. - : SAGE Publications, 2015 BASE Show details

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